def test_pca_variance_after_trim():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# set number of active components
model.trim_components(5)
# kept variance must be smaller than total variance
assert(model.variance() < model.original_variance())
# kept variance ratio must be smaller than 1.0
assert(model.variance_ratio() < 1.0)
# noise variance must be bigger than 0.0
assert(model.noise_variance() > 0.0)
# noise variance ratio must also be bigger than 0.0
assert(model.noise_variance_ratio() > 0.0)
# inverse noise variance is computable
assert(model.inverse_noise_variance() == 1 / model.noise_variance())
def test_pca_inverse_noise_variance():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
# inverse noise_variance it's not computable
model.inverse_noise_variance()
def test_pca_inverse_noise_variance():
samples = [PointCloud(np.random.randn(10)) for _ in range(10)]
model = PCAModel(samples)
with raises(ValueError):
# inverse noise_variance it's not computable
model.inverse_noise_variance()
